Overview

Dataset statistics

Number of variables29
Number of observations809711
Missing cells0
Missing cells (%)0.0%
Duplicate rows1014
Duplicate rows (%)0.1%
Total size in memory179.2 MiB
Average record size in memory232.0 B

Variable types

Numeric24
Categorical5

Alerts

Dataset has 1014 (0.1%) duplicate rowsDuplicates
DIST_CIA is highly correlated with DIST_HECHOHigh correlation
DIST_HECHO is highly correlated with DIST_CIAHigh correlation
DPTO_CIA is highly correlated with DPTO_HECHO and 3 other fieldsHigh correlation
DPTO_HECHO is highly correlated with DPTO_CIA and 3 other fieldsHigh correlation
LIBRO is highly correlated with TIPO_DENUNCIAHigh correlation
PROV_CIA is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
PROV_HECHO is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
REGION is highly correlated with DPTO_CIA and 1 other fieldsHigh correlation
TIPO is highly correlated with FEC_REGISTRO_ANIO and 1 other fieldsHigh correlation
TIPO_DENUNCIA is highly correlated with LIBROHigh correlation
FEC_REGISTRO_ANIO is highly correlated with TIPO and 1 other fieldsHigh correlation
FEC_REGISTRO_MES is highly correlated with FECHA_HORA_HECHO_MESHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with TIPO and 1 other fieldsHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with FEC_REGISTRO_MESHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
DIST_CIA is highly correlated with DIST_HECHOHigh correlation
DIST_HECHO is highly correlated with DIST_CIAHigh correlation
DPTO_CIA is highly correlated with DPTO_HECHO and 3 other fieldsHigh correlation
DPTO_HECHO is highly correlated with DPTO_CIA and 3 other fieldsHigh correlation
LIBRO is highly correlated with TIPO_DENUNCIAHigh correlation
PROV_CIA is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
PROV_HECHO is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
REGION is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
TIPO is highly correlated with REGION and 2 other fieldsHigh correlation
TIPO_DENUNCIA is highly correlated with LIBROHigh correlation
FEC_REGISTRO_ANIO is highly correlated with TIPO and 1 other fieldsHigh correlation
FEC_REGISTRO_MES is highly correlated with FECHA_HORA_HECHO_MESHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with TIPO and 1 other fieldsHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with FEC_REGISTRO_MESHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
DIST_CIA is highly correlated with DIST_HECHOHigh correlation
DIST_HECHO is highly correlated with DIST_CIAHigh correlation
DPTO_CIA is highly correlated with DPTO_HECHO and 3 other fieldsHigh correlation
DPTO_HECHO is highly correlated with DPTO_CIA and 3 other fieldsHigh correlation
LIBRO is highly correlated with TIPO_DENUNCIAHigh correlation
PROV_CIA is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
PROV_HECHO is highly correlated with DPTO_CIA and 2 other fieldsHigh correlation
REGION is highly correlated with DPTO_CIA and 1 other fieldsHigh correlation
TIPO is highly correlated with FEC_REGISTRO_ANIO and 1 other fieldsHigh correlation
TIPO_DENUNCIA is highly correlated with LIBROHigh correlation
FEC_REGISTRO_ANIO is highly correlated with TIPO and 1 other fieldsHigh correlation
FEC_REGISTRO_MES is highly correlated with FECHA_HORA_HECHO_MESHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with TIPO and 1 other fieldsHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with FEC_REGISTRO_MESHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
FEC_REGISTRO_ANIO is highly correlated with TIPOHigh correlation
TIPO is highly correlated with FEC_REGISTRO_ANIOHigh correlation
COMISARIA is highly correlated with DIST_CIA and 3 other fieldsHigh correlation
DERIVADA_FISCALIA is highly correlated with EST_CIVILHigh correlation
DIRECCION is highly correlated with DIST_CIAHigh correlation
DIST_CIA is highly correlated with COMISARIA and 7 other fieldsHigh correlation
DIST_HECHO is highly correlated with COMISARIA and 6 other fieldsHigh correlation
DPTO_CIA is highly correlated with DIST_CIA and 5 other fieldsHigh correlation
DPTO_HECHO is highly correlated with DIST_CIA and 5 other fieldsHigh correlation
EST_CIVIL is highly correlated with DERIVADA_FISCALIAHigh correlation
LIBRO is highly correlated with TIPO_DENUNCIAHigh correlation
PROV_CIA is highly correlated with COMISARIA and 6 other fieldsHigh correlation
PROV_HECHO is highly correlated with COMISARIA and 6 other fieldsHigh correlation
REGION is highly correlated with DIST_CIA and 7 other fieldsHigh correlation
SEXO is highly correlated with SIT_PERSONAHigh correlation
SIT_PERSONA is highly correlated with SEXOHigh correlation
TIPO is highly correlated with REGION and 1 other fieldsHigh correlation
TIPO_DENUNCIA is highly correlated with LIBROHigh correlation
FEC_REGISTRO_ANIO is highly correlated with REGION and 2 other fieldsHigh correlation
FEC_REGISTRO_MES is highly correlated with FECHA_HORA_HECHO_MESHigh correlation
FEC_REGISTRO_DIA is highly correlated with FECHA_HORA_HECHO_DIAHigh correlation
FEC_REGISTRO_DIA_SEM is highly correlated with FECHA_HORA_HECHO_DIA_SEMHigh correlation
FECHA_HORA_HECHO_ANIO is highly correlated with FEC_REGISTRO_ANIOHigh correlation
FECHA_HORA_HECHO_MES is highly correlated with FEC_REGISTRO_MESHigh correlation
FECHA_HORA_HECHO_DIA is highly correlated with FEC_REGISTRO_DIAHigh correlation
FECHA_HORA_HECHO_DIA_SEM is highly correlated with FEC_REGISTRO_DIA_SEMHigh correlation
DPTO_CIA has 8250 (1.0%) zeros Zeros
DPTO_HECHO has 8124 (1.0%) zeros Zeros
EST_CIVIL has 136351 (16.8%) zeros Zeros
VIA has 20641 (2.5%) zeros Zeros
FEC_REGISTRO_DIA_SEM has 139264 (17.2%) zeros Zeros
FECHA_HORA_HECHO_DIA_SEM has 123397 (15.2%) zeros Zeros

Reproduction

Analysis started2022-08-02 22:11:38.146512
Analysis finished2022-08-02 22:16:04.885771
Duration4 minutes and 26.74 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

COMISARIA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1104
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean525.6741788
Minimum0
Maximum1103
Zeros3666
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:04.976770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile61
Q1231
median492
Q3837
95-th percentile1059
Maximum1103
Range1103
Interquartile range (IQR)606

Descriptive statistics

Standard deviation325.5309107
Coefficient of variation (CV)0.6192636501
Kurtosis-1.258048507
Mean525.6741788
Median Absolute Deviation (MAD)266
Skewness0.1906195132
Sum425644165
Variance105970.3738
MonotonicityNot monotonic
2022-08-02T17:16:05.102768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23151606
 
6.4%
28112659
 
1.6%
95410189
 
1.3%
10678917
 
1.1%
777764
 
1.0%
2387718
 
1.0%
8977476
 
0.9%
3686538
 
0.8%
4955986
 
0.7%
4315932
 
0.7%
Other values (1094)684926
84.6%
ValueCountFrequency (%)
03666
0.5%
11672
0.2%
21366
 
0.2%
31940
0.2%
46
 
< 0.1%
5114
 
< 0.1%
62553
0.3%
757
 
< 0.1%
8345
 
< 0.1%
9108
 
< 0.1%
ValueCountFrequency (%)
11032
 
< 0.1%
110247
 
< 0.1%
1101729
 
0.1%
1100118
 
< 0.1%
10991715
0.2%
10981851
0.2%
10974058
0.5%
10962646
0.3%
10951082
 
0.1%
109458
 
< 0.1%

DERIVADA_FISCALIA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.794043801
Minimum0
Maximum9
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:05.200768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q14
median4
Q37
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.443064563
Coefficient of variation (CV)0.3010119689
Kurtosis-0.1248069762
Mean4.794043801
Median Absolute Deviation (MAD)0
Skewness1.0657851
Sum3881790
Variance2.082435334
MonotonicityNot monotonic
2022-08-02T17:16:05.275799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4544548
67.3%
7193527
 
23.9%
324335
 
3.0%
521948
 
2.7%
914614
 
1.8%
27382
 
0.9%
63301
 
0.4%
144
 
< 0.1%
09
 
< 0.1%
83
 
< 0.1%
ValueCountFrequency (%)
09
 
< 0.1%
144
 
< 0.1%
27382
 
0.9%
324335
 
3.0%
4544548
67.3%
521948
 
2.7%
63301
 
0.4%
7193527
 
23.9%
83
 
< 0.1%
914614
 
1.8%
ValueCountFrequency (%)
914614
 
1.8%
83
 
< 0.1%
7193527
 
23.9%
63301
 
0.4%
521948
 
2.7%
4544548
67.3%
324335
 
3.0%
27382
 
0.9%
144
 
< 0.1%
09
 
< 0.1%

DIRECCION
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1712
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean805.1322497
Minimum0
Maximum1711
Zeros173
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:05.377769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q1327
median785
Q31239
95-th percentile1616
Maximum1711
Range1711
Interquartile range (IQR)912

Descriptive statistics

Standard deviation513.0744349
Coefficient of variation (CV)0.6372548549
Kurtosis-1.282829346
Mean805.1322497
Median Absolute Deviation (MAD)455
Skewness0.1086307905
Sum651924439
Variance263245.3757
MonotonicityNot monotonic
2022-08-02T17:16:05.493768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
508663
 
1.1%
5248431
 
1.0%
15498210
 
1.0%
2347718
 
1.0%
5687526
 
0.9%
11226692
 
0.8%
9806597
 
0.8%
6966547
 
0.8%
1245869
 
0.7%
3115623
 
0.7%
Other values (1702)737835
91.1%
ValueCountFrequency (%)
0173
 
< 0.1%
1255
< 0.1%
2294
< 0.1%
3396
< 0.1%
491
 
< 0.1%
551
 
< 0.1%
6212
 
< 0.1%
7321
< 0.1%
8569
0.1%
91
 
< 0.1%
ValueCountFrequency (%)
171189
 
< 0.1%
1710124
 
< 0.1%
170959
 
< 0.1%
170829
 
< 0.1%
17076
 
< 0.1%
17064181
0.5%
1705130
 
< 0.1%
1704342
 
< 0.1%
170381
 
< 0.1%
1702173
 
< 0.1%

DIST_CIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct816
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean392.7146859
Minimum0
Maximum815
Zeros4085
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:05.622771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42
Q1161
median368
Q3639
95-th percentile777
Maximum815
Range815
Interquartile range (IQR)478

Descriptive statistics

Standard deviation245.5110573
Coefficient of variation (CV)0.6251639325
Kurtosis-1.35002112
Mean392.7146859
Median Absolute Deviation (MAD)232
Skewness0.07684469637
Sum317985401
Variance60275.67928
MonotonicityNot monotonic
2022-08-02T17:16:05.742799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63931280
 
3.9%
36821554
 
2.7%
4220693
 
2.6%
8020420
 
2.5%
38318345
 
2.3%
14115714
 
1.9%
18014893
 
1.8%
54312829
 
1.6%
77911836
 
1.5%
64611824
 
1.5%
Other values (806)630323
77.8%
ValueCountFrequency (%)
04085
0.5%
130
 
< 0.1%
25432
0.7%
3118
 
< 0.1%
4328
 
< 0.1%
5108
 
< 0.1%
6139
 
< 0.1%
7111
 
< 0.1%
810
 
< 0.1%
927
 
< 0.1%
ValueCountFrequency (%)
8155
 
< 0.1%
81444
 
< 0.1%
813214
 
< 0.1%
81291
 
< 0.1%
8111856
0.2%
8101162
0.1%
8091148
0.1%
808456
 
0.1%
807788
0.1%
80666
 
< 0.1%

DIST_HECHO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1428
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean708.3835455
Minimum0
Maximum1427
Zeros3812
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:05.862799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79
Q1302
median688
Q31112
95-th percentile1369
Maximum1427
Range1427
Interquartile range (IQR)810

Descriptive statistics

Standard deviation428.8736679
Coefficient of variation (CV)0.6054257904
Kurtosis-1.319349098
Mean708.3835455
Median Absolute Deviation (MAD)424
Skewness0.001181408577
Sum573585949
Variance183932.623
MonotonicityNot monotonic
2022-08-02T17:16:05.970808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111230261
 
3.7%
7920376
 
2.5%
13719337
 
2.4%
112517765
 
2.2%
65617340
 
2.1%
32916191
 
2.0%
136913928
 
1.7%
68813356
 
1.6%
137013174
 
1.6%
95813061
 
1.6%
Other values (1418)634922
78.4%
ValueCountFrequency (%)
03812
0.5%
135
 
< 0.1%
25243
0.6%
3116
 
< 0.1%
419
 
< 0.1%
520
 
< 0.1%
6296
 
< 0.1%
74
 
< 0.1%
816
 
< 0.1%
956
 
< 0.1%
ValueCountFrequency (%)
14274
 
< 0.1%
142649
 
< 0.1%
1425748
0.1%
1424116
 
< 0.1%
14231774
0.2%
142213
 
< 0.1%
14211
 
< 0.1%
14201160
0.1%
14193
 
< 0.1%
141810
 
< 0.1%

DPTO_CIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.63762379
Minimum0
Maximum25
Zeros8250
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:06.075769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median14
Q314
95-th percentile21
Maximum25
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.79571199
Coefficient of variation (CV)0.4980150667
Kurtosis-0.4469741801
Mean11.63762379
Median Absolute Deviation (MAD)3
Skewness-0.1065826136
Sum9423112
Variance33.59027747
MonotonicityNot monotonic
2022-08-02T17:16:06.170769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
14302953
37.4%
376022
 
9.4%
2043124
 
5.3%
738287
 
4.7%
1236855
 
4.6%
1336477
 
4.5%
636002
 
4.4%
1134246
 
4.2%
1031257
 
3.9%
128736
 
3.5%
Other values (16)145752
18.0%
ValueCountFrequency (%)
08250
 
1.0%
128736
 
3.5%
212394
 
1.5%
376022
9.4%
410718
 
1.3%
518867
 
2.3%
636002
4.4%
738287
4.7%
82637
 
0.3%
912154
 
1.5%
ValueCountFrequency (%)
257640
 
0.9%
248322
 
1.0%
2315011
 
1.9%
229189
 
1.1%
2114627
 
1.8%
2043124
5.3%
193301
 
0.4%
188088
 
1.0%
173964
 
0.5%
16406
 
0.1%

DPTO_HECHO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.761384
Minimum0
Maximum26
Zeros8124
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:06.265803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q17
median14
Q314
95-th percentile22
Maximum26
Range26
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.018374647
Coefficient of variation (CV)0.5117063304
Kurtosis-0.3486052785
Mean11.761384
Median Absolute Deviation (MAD)3
Skewness0.03262340619
Sum9523322
Variance36.22083339
MonotonicityNot monotonic
2022-08-02T17:16:06.360770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
14301847
37.3%
376280
 
9.4%
2143288
 
5.3%
738251
 
4.7%
1236851
 
4.6%
1336832
 
4.5%
636011
 
4.4%
1134426
 
4.3%
1031302
 
3.9%
129024
 
3.6%
Other values (17)145599
18.0%
ValueCountFrequency (%)
08124
 
1.0%
129024
 
3.6%
212218
 
1.5%
376280
9.4%
410815
 
1.3%
518792
 
2.3%
636011
4.4%
738251
4.7%
82653
 
0.3%
912149
 
1.5%
ValueCountFrequency (%)
267660
 
0.9%
258323
 
1.0%
2415040
 
1.9%
239276
 
1.1%
2214671
 
1.8%
2143288
5.3%
203354
 
0.4%
198
 
< 0.1%
188053
 
1.0%
173966
 
0.5%

EDAD
Real number (ℝ≥0)

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.9457646
Minimum18
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:06.468769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q127
median32
Q341
95-th percentile57
Maximum75
Range57
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.42874197
Coefficient of variation (CV)0.3270422641
Kurtosis0.5254670608
Mean34.9457646
Median Absolute Deviation (MAD)7
Skewness0.8824736196
Sum28295970
Variance130.6161431
MonotonicityNot monotonic
2022-08-02T17:16:06.576799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3188579
 
10.9%
3025454
 
3.1%
2725416
 
3.1%
2825339
 
3.1%
2925250
 
3.1%
3224964
 
3.1%
2624955
 
3.1%
3324362
 
3.0%
2424248
 
3.0%
2524202
 
3.0%
Other values (48)496942
61.4%
ValueCountFrequency (%)
1817183
2.1%
1918809
2.3%
2020708
2.6%
2121788
2.7%
2223097
2.9%
2323857
2.9%
2424248
3.0%
2524202
3.0%
2624955
3.1%
2725416
3.1%
ValueCountFrequency (%)
75861
0.1%
741071
0.1%
731103
0.1%
721216
0.2%
711361
0.2%
701396
0.2%
691586
0.2%
681707
0.2%
671792
0.2%
662022
0.2%

EST_CIVIL
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.756524241
Minimum0
Maximum6
Zeros136351
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:06.670798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q35
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.051465432
Coefficient of variation (CV)0.5461073323
Kurtosis-0.7052627201
Mean3.756524241
Median Absolute Deviation (MAD)0
Skewness-1.09258905
Sum3041699
Variance4.208510419
MonotonicityNot monotonic
2022-08-02T17:16:06.742799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5571263
70.6%
0136351
 
16.8%
172767
 
9.0%
417355
 
2.1%
27161
 
0.9%
64811
 
0.6%
33
 
< 0.1%
ValueCountFrequency (%)
0136351
 
16.8%
172767
 
9.0%
27161
 
0.9%
33
 
< 0.1%
417355
 
2.1%
5571263
70.6%
64811
 
0.6%
ValueCountFrequency (%)
64811
 
0.6%
5571263
70.6%
417355
 
2.1%
33
 
< 0.1%
27161
 
0.9%
172767
 
9.0%
0136351
 
16.8%

LIBRO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct55
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.70259636
Minimum0
Maximum54
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:07.321801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q128
median35
Q335
95-th percentile37
Maximum54
Range54
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.657561619
Coefficient of variation (CV)0.2100005168
Kurtosis7.086682437
Mean31.70259636
Median Absolute Deviation (MAD)0
Skewness-2.509810094
Sum25669941
Variance44.32312671
MonotonicityNot monotonic
2022-08-02T17:16:07.436799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35440843
54.4%
28183958
22.7%
3057805
 
7.1%
3233195
 
4.1%
4031752
 
3.9%
924609
 
3.0%
412030
 
1.5%
396859
 
0.8%
64025
 
0.5%
333911
 
0.5%
Other values (45)10724
 
1.3%
ValueCountFrequency (%)
01
 
< 0.1%
11
 
< 0.1%
21
 
< 0.1%
331
 
< 0.1%
412030
1.5%
519
 
< 0.1%
64025
 
0.5%
72
 
< 0.1%
82
 
< 0.1%
924609
3.0%
ValueCountFrequency (%)
547
 
< 0.1%
531
 
< 0.1%
5240
 
< 0.1%
5111
 
< 0.1%
5067
 
< 0.1%
492
 
< 0.1%
48126
< 0.1%
471
 
< 0.1%
46238
< 0.1%
454
 
< 0.1%

PROV_CIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct188
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.09852528
Minimum0
Maximum187
Zeros7118
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:07.550809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q147
median107
Q3107
95-th percentile175
Maximum187
Range187
Interquartile range (IQR)60

Descriptive statistics

Standard deviation47.78139179
Coefficient of variation (CV)0.5188073494
Kurtosis-0.7148460057
Mean92.09852528
Median Absolute Deviation (MAD)29
Skewness-0.157851791
Sum74573189
Variance2283.061401
MonotonicityNot monotonic
2022-08-02T17:16:07.661771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107280012
34.6%
1163260
 
7.8%
2736002
 
4.4%
4628972
 
3.6%
17526810
 
3.3%
14124363
 
3.0%
6222745
 
2.8%
8120063
 
2.5%
16714867
 
1.8%
9214822
 
1.8%
Other values (178)277795
34.3%
ValueCountFrequency (%)
07118
0.9%
1240
 
< 0.1%
2210
 
< 0.1%
351
 
< 0.1%
41162
 
0.1%
5489
 
0.1%
63682
0.5%
711
 
< 0.1%
81332
 
0.2%
964
 
< 0.1%
ValueCountFrequency (%)
1872037
0.3%
186494
 
0.1%
1851159
 
0.1%
18494
 
< 0.1%
183649
 
0.1%
18264
 
< 0.1%
1811631
0.2%
18048
 
< 0.1%
1792423
0.3%
1783237
0.4%

PROV_HECHO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct192
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.87380312
Minimum0
Maximum191
Zeros6965
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:07.808770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q149
median109
Q3109
95-th percentile178
Maximum191
Range191
Interquartile range (IQR)60

Descriptive statistics

Standard deviation48.464819
Coefficient of variation (CV)0.516276292
Kurtosis-0.6902616048
Mean93.87380312
Median Absolute Deviation (MAD)29
Skewness-0.1662726823
Sum76010651
Variance2348.83868
MonotonicityNot monotonic
2022-08-02T17:16:07.947769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109278765
34.4%
1163738
 
7.9%
2836011
 
4.4%
4829205
 
3.6%
17828224
 
3.5%
14324452
 
3.0%
6422465
 
2.8%
8320889
 
2.6%
17014872
 
1.8%
9414751
 
1.8%
Other values (182)276339
34.1%
ValueCountFrequency (%)
06965
0.9%
1258
 
< 0.1%
2206
 
< 0.1%
350
 
< 0.1%
41166
 
0.1%
5543
 
0.1%
63686
0.5%
711
 
< 0.1%
81359
 
0.2%
965
 
< 0.1%
ValueCountFrequency (%)
1912027
0.3%
190518
 
0.1%
1891144
0.1%
188119
 
< 0.1%
187651
 
0.1%
18653
 
< 0.1%
1851526
0.2%
1843
 
< 0.1%
18364
 
< 0.1%
1822474
0.3%

REGION
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.73021238
Minimum0
Maximum55
Zeros7359
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:08.074801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q119
median24
Q333
95-th percentile50
Maximum55
Range55
Interquartile range (IQR)14

Descriptive statistics

Standard deviation12.4659511
Coefficient of variation (CV)0.4663618427
Kurtosis-0.3749300912
Mean26.73021238
Median Absolute Deviation (MAD)7
Skewness0.6202052988
Sum21643747
Variance155.3999369
MonotonicityNot monotonic
2022-08-02T17:16:08.185772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24226344
28.0%
4667953
 
8.4%
1459797
 
7.4%
2730717
 
3.8%
1627896
 
3.4%
2327601
 
3.4%
2227144
 
3.4%
1724532
 
3.0%
2123620
 
2.9%
1922881
 
2.8%
Other values (46)271226
33.5%
ValueCountFrequency (%)
07359
0.9%
164
 
< 0.1%
21
 
< 0.1%
35
 
< 0.1%
44682
0.6%
53893
0.5%
66779
0.8%
71957
 
0.2%
84858
0.6%
91693
 
0.2%
ValueCountFrequency (%)
556368
 
0.8%
5415061
 
1.9%
5310431
 
1.3%
525642
 
0.7%
512383
 
0.3%
5012593
 
1.6%
49851
 
0.1%
481914
 
0.2%
474370
 
0.5%
4667953
8.4%

SEXO
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
1
413567 
0
396144 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters809711
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1413567
51.1%
0396144
48.9%

Length

2022-08-02T17:16:08.293768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-02T17:16:08.402768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1413567
51.1%
0396144
48.9%

Most occurring characters

ValueCountFrequency (%)
1413567
51.1%
0396144
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number809711
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1413567
51.1%
0396144
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common809711
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1413567
51.1%
0396144
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII809711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1413567
51.1%
0396144
48.9%

SIT_PERSONA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.654915149
Minimum0
Maximum40
Zeros185
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:08.506769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median7
Q38
95-th percentile8
Maximum40
Range40
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.740018185
Coefficient of variation (CV)0.4885773535
Kurtosis35.09568731
Mean7.654915149
Median Absolute Deviation (MAD)0
Skewness4.928544078
Sum6198269
Variance13.98773602
MonotonicityNot monotonic
2022-08-02T17:16:08.620801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
7406431
50.2%
8322532
39.8%
144854
 
5.5%
117051
 
0.9%
166987
 
0.9%
144808
 
0.6%
343790
 
0.5%
242938
 
0.4%
371825
 
0.2%
221655
 
0.2%
Other values (31)6840
 
0.8%
ValueCountFrequency (%)
0185
 
< 0.1%
144854
 
5.5%
2353
 
< 0.1%
37
 
< 0.1%
461
 
< 0.1%
546
 
< 0.1%
6153
 
< 0.1%
7406431
50.2%
8322532
39.8%
9574
 
0.1%
ValueCountFrequency (%)
401109
 
0.1%
39853
 
0.1%
383
 
< 0.1%
371825
0.2%
367
 
< 0.1%
35112
 
< 0.1%
343790
0.5%
3343
 
< 0.1%
32240
 
< 0.1%
31238
 
< 0.1%

SUB_TIPO
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
1
805878 
0
 
3833

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters809711
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1805878
99.5%
03833
 
0.5%

Length

2022-08-02T17:16:08.729769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-02T17:16:08.824799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1805878
99.5%
03833
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1805878
99.5%
03833
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number809711
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1805878
99.5%
03833
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common809711
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1805878
99.5%
03833
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII809711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1805878
99.5%
03833
 
0.5%

TIPO
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
1
568362 
0
241349 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters809711
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1568362
70.2%
0241349
29.8%

Length

2022-08-02T17:16:08.904769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-02T17:16:08.999771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1568362
70.2%
0241349
29.8%

Most occurring characters

ValueCountFrequency (%)
1568362
70.2%
0241349
29.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number809711
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1568362
70.2%
0241349
29.8%

Most occurring scripts

ValueCountFrequency (%)
Common809711
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1568362
70.2%
0241349
29.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII809711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1568362
70.2%
0241349
29.8%

TIPO_DENUNCIA
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
2
505961 
0
197304 
1
62576 
3
 
43870

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters809711
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row2
4th row0
5th row2

Common Values

ValueCountFrequency (%)
2505961
62.5%
0197304
 
24.4%
162576
 
7.7%
343870
 
5.4%

Length

2022-08-02T17:16:09.081804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-02T17:16:09.180771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2505961
62.5%
0197304
 
24.4%
162576
 
7.7%
343870
 
5.4%

Most occurring characters

ValueCountFrequency (%)
2505961
62.5%
0197304
 
24.4%
162576
 
7.7%
343870
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number809711
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2505961
62.5%
0197304
 
24.4%
162576
 
7.7%
343870
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common809711
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2505961
62.5%
0197304
 
24.4%
162576
 
7.7%
343870
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII809711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2505961
62.5%
0197304
 
24.4%
162576
 
7.7%
343870
 
5.4%

UBICACION
Real number (ℝ≥0)

Distinct737837
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean369081.2812
Minimum0
Maximum737836
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:09.286802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37505.5
Q1185653.5
median369070
Q3554171.5
95-th percentile699231.5
Maximum737836
Range737836
Interquartile range (IQR)368518

Descriptive statistics

Standard deviation211909.6607
Coefficient of variation (CV)0.574154452
Kurtosis-1.193609155
Mean369081.2812
Median Absolute Deviation (MAD)184253
Skewness-0.004298594752
Sum2.988491733 × 1011
Variance4.490570432 × 1010
MonotonicityNot monotonic
2022-08-02T17:16:09.400810image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178750331
 
< 0.1%
577597276
 
< 0.1%
641933256
 
< 0.1%
646424228
 
< 0.1%
268137211
 
< 0.1%
652162196
 
< 0.1%
513416194
 
< 0.1%
585117179
 
< 0.1%
7574177
 
< 0.1%
614544175
 
< 0.1%
Other values (737827)807488
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
7378361
 
< 0.1%
7378351
 
< 0.1%
7378341
 
< 0.1%
7378331
 
< 0.1%
7378321
 
< 0.1%
7378311
 
< 0.1%
7378304
< 0.1%
7378291
 
< 0.1%
7378283
< 0.1%
7378271
 
< 0.1%

VIA
Real number (ℝ≥0)

ZEROS

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.723146901
Minimum0
Maximum15
Zeros20641
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:09.509768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median10
Q310
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.653429871
Coefficient of variation (CV)0.4730493823
Kurtosis-0.8301208701
Mean7.723146901
Median Absolute Deviation (MAD)0
Skewness-0.7081578929
Sum6253517
Variance13.34754982
MonotonicityNot monotonic
2022-08-02T17:16:09.594770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
10467479
57.7%
2124553
 
15.4%
370912
 
8.8%
860481
 
7.5%
020641
 
2.5%
1316525
 
2.0%
614783
 
1.8%
1512941
 
1.6%
57260
 
0.9%
75097
 
0.6%
Other values (6)9039
 
1.1%
ValueCountFrequency (%)
020641
 
2.5%
12452
 
0.3%
2124553
15.4%
370912
8.8%
42405
 
0.3%
57260
 
0.9%
614783
 
1.8%
75097
 
0.6%
860481
7.5%
9348
 
< 0.1%
ValueCountFrequency (%)
1512941
 
1.6%
141580
 
0.2%
1316525
 
2.0%
121302
 
0.2%
11952
 
0.1%
10467479
57.7%
9348
 
< 0.1%
860481
 
7.5%
75097
 
0.6%
614783
 
1.8%

PAIS_NATAL
Real number (ℝ≥0)

Distinct211
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.0206938
Minimum0
Maximum210
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:09.712770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile145
Q1145
median145
Q3145
95-th percentile145
Maximum210
Range210
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.152173713
Coefficient of variation (CV)0.04242272983
Kurtosis238.1543263
Mean145.0206938
Median Absolute Deviation (MAD)0
Skewness-7.708023326
Sum117424851
Variance37.8492414
MonotonicityNot monotonic
2022-08-02T17:16:09.836769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145804134
99.3%
2032464
 
0.3%
201841
 
0.1%
49387
 
< 0.1%
9217
 
< 0.1%
67190
 
< 0.1%
22158
 
< 0.1%
41151
 
< 0.1%
7694
 
< 0.1%
2688
 
< 0.1%
Other values (201)987
 
0.1%
ValueCountFrequency (%)
01
 
< 0.1%
17
 
< 0.1%
22
 
< 0.1%
323
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
9217
< 0.1%
ValueCountFrequency (%)
2101
 
< 0.1%
2091
 
< 0.1%
2081
 
< 0.1%
2075
 
< 0.1%
2061
 
< 0.1%
2051
 
< 0.1%
2041
 
< 0.1%
2032464
0.3%
2021
 
< 0.1%
201841
 
0.1%

FEC_REGISTRO_ANIO
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
2019
289569 
2018
207252 
2017
179675 
2016
133213 
2014
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3238844
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2019289569
35.8%
2018207252
25.6%
2017179675
22.2%
2016133213
16.5%
20142
 
< 0.1%

Length

2022-08-02T17:16:09.948770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-02T17:16:10.067769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2019289569
35.8%
2018207252
25.6%
2017179675
22.2%
2016133213
16.5%
20142
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2809711
25.0%
0809711
25.0%
1809711
25.0%
9289569
 
8.9%
8207252
 
6.4%
7179675
 
5.5%
6133213
 
4.1%
42
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3238844
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2809711
25.0%
0809711
25.0%
1809711
25.0%
9289569
 
8.9%
8207252
 
6.4%
7179675
 
5.5%
6133213
 
4.1%
42
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common3238844
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2809711
25.0%
0809711
25.0%
1809711
25.0%
9289569
 
8.9%
8207252
 
6.4%
7179675
 
5.5%
6133213
 
4.1%
42
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3238844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2809711
25.0%
0809711
25.0%
1809711
25.0%
9289569
 
8.9%
8207252
 
6.4%
7179675
 
5.5%
6133213
 
4.1%
42
 
< 0.1%

FEC_REGISTRO_MES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.570191093
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:10.182770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.556472892
Coefficient of variation (CV)0.5413043307
Kurtosis-1.30146072
Mean6.570191093
Median Absolute Deviation (MAD)3
Skewness0.01865577497
Sum5319956
Variance12.64849943
MonotonicityNot monotonic
2022-08-02T17:16:10.264768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1280066
9.9%
1176406
9.4%
1074636
9.2%
373871
9.1%
470909
8.8%
569278
8.6%
868356
8.4%
266856
8.3%
666456
8.2%
166445
8.2%
Other values (2)96432
11.9%
ValueCountFrequency (%)
166445
8.2%
266856
8.3%
373871
9.1%
470909
8.8%
569278
8.6%
666456
8.2%
746465
5.7%
868356
8.4%
949967
6.2%
1074636
9.2%
ValueCountFrequency (%)
1280066
9.9%
1176406
9.4%
1074636
9.2%
949967
6.2%
868356
8.4%
746465
5.7%
666456
8.2%
569278
8.6%
470909
8.8%
373871
9.1%

FEC_REGISTRO_DIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.76319205
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:10.360769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.78737789
Coefficient of variation (CV)0.5574618301
Kurtosis-1.191214714
Mean15.76319205
Median Absolute Deviation (MAD)8
Skewness0.002019229053
Sum12763630
Variance77.21801018
MonotonicityNot monotonic
2022-08-02T17:16:10.452768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1827588
 
3.4%
427338
 
3.4%
1927238
 
3.4%
2227226
 
3.4%
2627043
 
3.3%
1127001
 
3.3%
2826994
 
3.3%
2026906
 
3.3%
526839
 
3.3%
1726780
 
3.3%
Other values (21)538758
66.5%
ValueCountFrequency (%)
125421
3.1%
226617
3.3%
326570
3.3%
427338
3.4%
526839
3.3%
626451
3.3%
726245
3.2%
825855
3.2%
926606
3.3%
1026099
3.2%
ValueCountFrequency (%)
3115991
2.0%
3023951
3.0%
2924705
3.1%
2826994
3.3%
2726550
3.3%
2627043
3.3%
2526303
3.2%
2425920
3.2%
2326493
3.3%
2227226
3.4%

FEC_REGISTRO_DIA_SEM
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.793853609
Minimum0
Maximum6
Zeros139264
Zeros (%)17.2%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:10.545768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.011049755
Coefficient of variation (CV)0.7198121433
Kurtosis-1.231037702
Mean2.793853609
Median Absolute Deviation (MAD)2
Skewness0.1337823363
Sum2262214
Variance4.044321116
MonotonicityNot monotonic
2022-08-02T17:16:10.620802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0139264
17.2%
1123634
15.3%
2119862
14.8%
3115677
14.3%
4109496
13.5%
6104951
13.0%
596827
12.0%
ValueCountFrequency (%)
0139264
17.2%
1123634
15.3%
2119862
14.8%
3115677
14.3%
4109496
13.5%
596827
12.0%
6104951
13.0%
ValueCountFrequency (%)
6104951
13.0%
596827
12.0%
4109496
13.5%
3115677
14.3%
2119862
14.8%
1123634
15.3%
0139264
17.2%

FECHA_HORA_HECHO_ANIO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.795316
Minimum1990
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:10.712798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile2016
Q12017
median2018
Q32019
95-th percentile2019
Maximum2019
Range29
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.114634399
Coefficient of variation (CV)0.0005524021144
Kurtosis3.45860723
Mean2017.795316
Median Absolute Deviation (MAD)1
Skewness-0.6262832874
Sum1633831063
Variance1.242409843
MonotonicityNot monotonic
2022-08-02T17:16:10.802798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2019288899
35.7%
2018204968
25.3%
2017179819
22.2%
2016134482
16.6%
20151292
 
0.2%
201476
 
< 0.1%
201333
 
< 0.1%
201228
 
< 0.1%
201122
 
< 0.1%
200718
 
< 0.1%
Other values (15)74
 
< 0.1%
ValueCountFrequency (%)
19905
< 0.1%
19961
 
< 0.1%
19973
< 0.1%
19981
 
< 0.1%
19991
 
< 0.1%
20003
< 0.1%
20012
 
< 0.1%
20024
< 0.1%
20033
< 0.1%
20044
< 0.1%
ValueCountFrequency (%)
2019288899
35.7%
2018204968
25.3%
2017179819
22.2%
2016134482
16.6%
20151292
 
0.2%
201476
 
< 0.1%
201333
 
< 0.1%
201228
 
< 0.1%
201122
 
< 0.1%
201010
 
< 0.1%

FECHA_HORA_HECHO_MES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.520591915
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:10.895800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.558400676
Coefficient of variation (CV)0.5457174322
Kurtosis-1.301972543
Mean6.520591915
Median Absolute Deviation (MAD)3
Skewness0.02634332052
Sum5279795
Variance12.66221537
MonotonicityNot monotonic
2022-08-02T17:16:10.976799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1176507
9.4%
1276396
9.4%
374629
9.2%
1073872
9.1%
470337
8.7%
169762
8.6%
568663
8.5%
267501
8.3%
866480
8.2%
665162
8.0%
Other values (2)100402
12.4%
ValueCountFrequency (%)
169762
8.6%
267501
8.3%
374629
9.2%
470337
8.7%
568663
8.5%
665162
8.0%
748499
6.0%
866480
8.2%
951903
6.4%
1073872
9.1%
ValueCountFrequency (%)
1276396
9.4%
1176507
9.4%
1073872
9.1%
951903
6.4%
866480
8.2%
748499
6.0%
665162
8.0%
568663
8.5%
470337
8.7%
374629
9.2%

FECHA_HORA_HECHO_DIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.6089506
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:11.071799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.812234928
Coefficient of variation (CV)0.5645629327
Kurtosis-1.192272551
Mean15.6089506
Median Absolute Deviation (MAD)8
Skewness0.009148324343
Sum12638739
Variance77.65548442
MonotonicityNot monotonic
2022-08-02T17:16:11.166799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
129807
 
3.7%
2527994
 
3.5%
1727556
 
3.4%
327424
 
3.4%
1527382
 
3.4%
1827250
 
3.4%
227145
 
3.4%
427134
 
3.4%
1126789
 
3.3%
1026725
 
3.3%
Other values (21)534505
66.0%
ValueCountFrequency (%)
129807
3.7%
227145
3.4%
327424
3.4%
427134
3.4%
526256
3.2%
625957
3.2%
726000
3.2%
826366
3.3%
926200
3.2%
1026725
3.3%
ValueCountFrequency (%)
3114322
1.8%
3023617
2.9%
2924371
3.0%
2826350
3.3%
2726113
3.2%
2626227
3.2%
2527994
3.5%
2426531
3.3%
2325652
3.2%
2225885
3.2%

FECHA_HORA_HECHO_DIA_SEM
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.129520286
Minimum0
Maximum6
Zeros123397
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2022-08-02T17:16:11.254798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.108682869
Coefficient of variation (CV)0.6738038665
Kurtosis-1.352700014
Mean3.129520286
Median Absolute Deviation (MAD)2
Skewness-0.05818490955
Sum2534007
Variance4.446543441
MonotonicityNot monotonic
2022-08-02T17:16:11.328771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6158198
19.5%
0123397
15.2%
5110623
13.7%
1107474
13.3%
2106539
13.2%
3102768
12.7%
4100712
12.4%
ValueCountFrequency (%)
0123397
15.2%
1107474
13.3%
2106539
13.2%
3102768
12.7%
4100712
12.4%
5110623
13.7%
6158198
19.5%
ValueCountFrequency (%)
6158198
19.5%
5110623
13.7%
4100712
12.4%
3102768
12.7%
2106539
13.2%
1107474
13.3%
0123397
15.2%

Interactions

2022-08-02T17:15:52.862549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:12:59.359802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:06.834647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:14.152181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:22.301146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:30.147175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:37.892712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:45.507745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:52.899750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:00.491716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:08.046749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:15.419742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:22.943712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:30.923753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:38.459756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:46.024301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:53.821297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:01.602325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:08.744298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:15.892298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:23.255583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:30.826921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:38.046846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:45.262847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:53.166552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:12:59.670799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:07.124677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:14.465148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-08-02T17:15:28.736924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:35.943166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:43.150843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:50.662848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:58.452799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:04.957798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:12.316145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:20.359148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:28.040149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:35.959713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:43.472745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:51.043711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:58.602714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:06.215713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:13.554713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:21.067752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:28.945662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:36.626756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:44.140300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:51.880299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:59.727298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:06.958328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:14.096297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:21.424555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:29.030957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:36.240169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:43.445844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:50.971875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:58.758772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:05.269802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:12.621147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:20.674182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:28.530175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:36.271717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:43.956751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:51.352712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:58.919749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:06.520729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:13.863744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:21.377713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:29.263661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:36.938759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:44.449296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:52.204301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:00.035297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:07.253329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:14.392297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:21.730548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:29.320931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:36.536871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:43.744843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:51.284841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:59.069771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:05.579805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:12.924149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:20.992150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:28.850146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:36.589713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:44.261724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:51.654714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:59.232744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:06.823751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:14.171711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:21.692743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:29.580673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:37.241751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:44.753299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:52.528298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:00.344299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:07.548332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:14.689297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:22.033553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:29.617922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:36.826846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:44.043844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:51.599841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:59.380769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:05.892835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:13.230175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:21.320154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:29.180152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:36.911715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:44.571713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:51.961714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:59.549712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:07.127748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:14.481714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:22.005745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:29.901782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:37.548756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:45.067335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:52.858295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:00.657301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:07.849300image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:14.985326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:22.341551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:29.920933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:37.127877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:44.340868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:51.921881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:59.692772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:06.204807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:13.535181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:21.649149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:29.506146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:37.229718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:44.883747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:52.271711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:59.864742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:07.431747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:14.793747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:22.317741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:30.234756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:37.856784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:45.386296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:53.183299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:00.986333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:08.148299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:15.283329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:22.647584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:30.222956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:37.430842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:44.642876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:52.233874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:59.999808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:06.522800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:13.841153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:21.976148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:29.829149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:37.564712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:45.195746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:13:52.581721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:00.185714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:07.740717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:15.104714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:22.629721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:30.582752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:38.158761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:45.696328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:14:53.508308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:01.298295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:08.447303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:15.582331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:22.953580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:30.528931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:37.735841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:44.946872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-08-02T17:15:52.551847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-08-02T17:16:11.445804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-02T17:16:11.723799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-02T17:16:11.996799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-02T17:16:12.250770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-08-02T17:16:12.379808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-08-02T17:16:00.222801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-02T17:16:01.986771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

COMISARIADERIVADA_FISCALIADIRECCIONDIST_CIADIST_HECHODPTO_CIADPTO_HECHOEDADEST_CIVILLIBROPROV_CIAPROV_HECHOREGIONSEXOSIT_PERSONASUB_TIPOTIPOTIPO_DENUNCIAUBICACIONVIAPAIS_NATALFEC_REGISTRO_ANIOFEC_REGISTRO_MESFEC_REGISTRO_DIAFEC_REGISTRO_DIA_SEMFECHA_HORA_HECHO_ANIOFECHA_HORA_HECHO_MESFECHA_HORA_HECHO_DIAFECHA_HORA_HECHO_DIA_SEM
041243052624821212321417517822170105993091014520161142016114
11096996356498614142553510710924080124695061014520161142016114
21096996356498614142053510710924080124506031014520161142016114
323141412457816112552815916111191106614221014520161142016114
41096996356498614145153510710924080125493911014520161142016114
51642137871154773902862641718010625581014520161142016114
661173276816591414315351071092408112183662101452016125201512254
73514568801376640535272816170124732111014520161252016114
815441027621107914142453510710924081125283741014520161362016151
94727124638869614142813510710924070125832461014520161362016125

Last rows

COMISARIADERIVADA_FISCALIADIRECCIONDIST_CIADIST_HECHODPTO_CIADPTO_HECHOEDADEST_CIVILLIBROPROV_CIAPROV_HECHOREGIONSEXOSIT_PERSONASUB_TIPOTIPOTIPO_DENUNCIAUBICACIONVIAPAIS_NATALFEC_REGISTRO_ANIOFEC_REGISTRO_MESFEC_REGISTRO_DIAFEC_REGISTRO_DIA_SEMFECHA_HORA_HECHO_ANIOFECHA_HORA_HECHO_MESFECHA_HORA_HECHO_DIAFECHA_HORA_HECHO_DIA_SEM
809701974415577211266232450530167170541710168261010145201912160201912156
809702974415577211266232433028167170540810028141010145201912182201912145
809703974415577211266232436528167170541710021146210145201912182201912171
80970497441557721126623245652816717054171001909791014520191222620197312
809705974715577211266232459528167170541710015002010145201912263201912252
809706974415577211266232430530167170541710119907010145201912300201912263
809707974715577211266232422528167170541710026633210145201912311201912311
8097089934715734128623243852817117454171002639741014520191290201911250
8097099934715734128623243659171174541210226697310145201912230201912226
80971099347157341286232437191711745411110268110710145201912285201912274

Duplicate rows

Most frequently occurring

COMISARIADERIVADA_FISCALIADIRECCIONDIST_CIADIST_HECHODPTO_CIADPTO_HECHOEDADEST_CIVILLIBROPROV_CIAPROV_HECHOREGIONSEXOSIT_PERSONASUB_TIPOTIPOTIPO_DENUNCIAUBICACIONVIAPAIS_NATALFEC_REGISTRO_ANIOFEC_REGISTRO_MESFEC_REGISTRO_DIAFEC_REGISTRO_DIA_SEMFECHA_HORA_HECHO_ANIOFECHA_HORA_HECHO_MESFECHA_HORA_HECHO_DIAFECHA_HORA_HECHO_DIA_SEM# duplicates
4174901452581131428159161111811011134721452016811320168807
237231422214124913135153546482308112457997314520168172201681617
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